Purpose To develop a deep learning model that automatically delineates the eight liver Couinaud segments and the spleen on CT for future liver remnant (FLR) volumetry. Materials and Methods In this retrospective study (January 2001 and October 2025), eight liver Couinaud segments and the spleen were manually labeled on CT scans of patients from Institution-A and the public Medical Segmentation Decathlon dataset. A 3D nnU-Net segmentation model was trained on this dataset and evaluated on three datasets (one internal and two external). Results The training dataset included 498 patients (442 from the public Medical Segmentation Decathlon dataset and 56 from Institution-A, mean age 55 ± 7 [SD] years, 38 males), while the testing dataset included 64 patients from Institution-A (50 had liver fibrosis and 8 underwent portal vein embolization; PVE), 197 patients from the publicly available colorectal liver metastases (CRLM) dataset (mean age 59 ± 12 years, 117 males), and 50 patients (25 were healthy and 25 had cirrhosis) from an external Institution-B (mean age 49 ± 9 years, 29 males). For the whole liver in Institution-A and Institution-B, Dice scores of 0.98 ± 0.02 (95% CI: 0.97, 0.99) and 0.98 ± 0.03 (95% CI: 0.97, 0.99), and 95% percentile Hausdorff Distance (HD) errors of 2.5 ± 3.8 mm (95% CI: 1.6, 3.3) and 3.3 ± 6.6 mm (95% CI: 1.4, 5.2) were obtained, respectively. The pre-PVE FLR% and post-PVE FLR% volume differences (manual vs automated, 8 patients) were 0.03 ± 2.4 and-0.39 ± 3.0, respectively. For the FLR in the CRLM dataset, a Dice score of 0.99 ± 0.01 (95% CI: 0.99, 0.993) and an HD error of 0.9 ± 1.8 mm (95% CI: 0.6, 1.1) were achieved. Conclusion The model accurately estimated preoperative FLR volumetry and generalized well to patients with colorectal liver metastases, fibrosis, cirrhosis and healthy controls. ©RSNA, 2026.